simulation wrapper, response time depending on arrival rate plot

src/main.py

@@ -1,6 +1,7 @@
 #!/bin/python3
 import random
 from heapq import heappush, heappop
+import matplotlib.pyplot as plt
 
 class Event:
     def __init__(self, event_type, request):
@@ -56,7 +57,7 @@ class Simulation:
         self.total_requests += 1
         if len(self.router_queue) == 0 and self.router_state == "idle":
             self.router_process(request)
-        elif ((len(self.router_queue) + + (self.router_state == "processing")) < 100):
+        elif ((len(self.router_queue) + (self.router_state == "processing")) < 100):
             self.router_queue.append(request)
         else:
             self.lost_requests += 1
@@ -128,6 +129,44 @@ class Simulation:
 
 
 
+def simulation_wrapper():
+    C_values = [1, 2, 3, 6]
+    simulation_time = 10000
+
+    lambda_vals = [l / 100 for l in range(1, 301)]
+
+    plt.figure(figsize=(10, 6))
+
+    for c in C_values:
+        lambda_values = []
+        mean_response_times = []
+        print(f"Processing C={c}...")
+        for lambda_val in lambda_vals:
+            try:
+                sim = Simulation(c, lambda_val)
+                sim.run(simulation_time)
+
+                if not sim.response_times:
+                    print(f"No completed requests for C={c}, λ={lambda_val:.2f}")
+                    continue
+
+                mean_rt = sum(sim.response_times) / len(sim.response_times)
+                lambda_values.append(lambda_val)
+                mean_response_times.append(mean_rt)
+                print(f"C={c}, λ={lambda_val:.2f}, Mean RT={mean_rt:.2f}, Loss Rate={sim.loss_rate:.2%}")
+            except ValueError as e:
+                print(f"Stopped at λ={lambda_val:.2f} for C={c}: {str(e)}")
+                break
+
+        plt.plot(lambda_values, mean_response_times, marker='.', linestyle='-', label=f'C={c}')
+
+    plt.xlabel('Arrival Rate (λ)')
+    plt.ylabel('Mean Response Time')
+    plt.title('Mean Response Time depending on Arrival Rate for different configurations of C (number of clusters)')
+    plt.legend()
+    plt.grid(True)
+    plt.show()
+